상세정보
학술지
The Signature from Messenger RNA Expression Profiling Can Predict Lymph Node Metastasis with High Accuracy for Non-small Cell Lung Cancer
- 저자
- 최내현, 손대순, 이진선, 송인승, 김경아, 박상호, 임유성, 서길주, 한정호, 김혜진, 이혜원, 강종호, 서정순, 김주한, 김진국
- 발행일
- 201609
- 출처
- Journal of Thoracic Oncology, v.1 no.7, pp.622-628
- ISSN
- 1556-0864
- 출판사
- Elsevier
- 협약과제
- 06MI1200, URC를 위한 내장형 컴포넌트 기술개발 및 표준화, 조영조
- 초록
- BACKGROUND: The extent of regional lymph node (LN) metastasis is the most important factor in the evaluation of resectability and prognosis of non-small cell lung cancer (NSCLC) to increase the chance of complete cure. The authors attempted to deduce a group of genes from the analysis of mRNA expression profiles of the tumor tissues of NSCLC patients with or without LN metastasis, and make a classification model for better prediction of LN metastasis. METHODS: The authors analyzed mRNA expression profiles of 79 NSCLC patients with or without LN metastasis, and deduced the gene signature for the predictive model of LN metastasis in lung cancer. The authors evaluated the predictive accuracy of each of four algorithms by applying them to another set of 33 NSCLC patients. Each algorithm's accuracy was calculated by 10-fold cross-validation, and a combined model showed a level of accuracy that was higher than any one of the better three component algorithms (i.e., ANN, DT, or NB). Avadis, SAS, ArrayXPath, and R-package were the statistical analysis software packages used. RESULTS: The authors selected 949 genes using a classical permutation t test (p < 0.01) and finally obtained a gene signature consisting of 31 genes by adjustment of multiple-hypothesis testing. The LN metastasis prediction model derived from the signature (31 genes) and their characteristic interactions provided a predictive accuracy of 84.85% when applied to a test set of 33 patients. CONCLUSIONS: The authors have demonstrated that their gene signature developed by the expression profiling of mRNAs from the primary tissue could predict the LN metastasis status of NSCLC. © 2006International Association for the Study of Lung Cancer.
- KSP 제안 키워드
- Analysis software, Classification models, Cross validation(CV), Expression profiles, Expression profiling, Gene signature, High accuracy, Hypothesis Testing, Level of accuracy, MRNA expression, Messenger RNA